How AI Works: What Artificial Intelligence Is and What It Can Do

In our everyday lives we meet artificial intelligence (AI) more and more often. When Netflix recommends a film, Facebook shows you an ad for a product you were thinking about recently, or when you use a voice assistant on your phone, artificial intelligence is working in the background. Let's take a look together at how AI actually works and what it really is.

Summary for Those Who Don't Have Time to Read the Whole Article

• Artificial intelligence works by processing large amounts of information and looking for recurring patterns in it.
• There are three basic types of AI learning: supervised (where we show it the correct answers), unsupervised (where it looks for connections on its own) and reinforcement learning (where it learns by trial and error).
• The brain of artificial intelligence is neural networks, which are inspired by how the human brain works.
• Ready-made AI solutions are fast and cheap, but custom-built systems solve a company's specific needs better.
• For a successful AI rollout you need quality data, clear goals and constant improvement of the system.

What Is Artificial Intelligence?

Artificial intelligence is a branch of computer science that tries to create systems capable of solving tasks that we humans would normally solve. It is not a magical or conscious being (as we often see in films), but a sophisticated tool built on four basic building blocks:

• Data – The information AI learns from. Artificial intelligence needs a huge number of examples to understand how things work. For example, to recognise faces, it must first see thousands of photographs of faces and learn what eyes, a nose or a mouth look like in various forms.
• Algorithms – The procedures for working with this information. These are sets of rules and mathematical procedures that AI uses to analyse data. Most often you will hear terms such as neural networks, machine learning or deep learning. These technologies enable AI to learn and improve based on experience.
• Computing power – The power of computers to process data. Today's AI would not be possible without powerful computers that can perform complex calculations very quickly. Cloud services such as Google Cloud or AWS provide the enormous computing power that AI needs to process millions of pieces of information.
• Outputs and predictions – The results of AI's work. After processing the data, artificial intelligence provides results, which can be forecasts, recommendations or decisions. For example, online shops use AI to offer products you might like, while analytics teams use it to estimate future trends.

How Does AI Learn?

The most widespread way artificial intelligence learns today is machine learning (ML). Unlike traditional programming, where the programmer writes exact instructions "when A happens, do B", machine learning works differently:

• AI is given a large number of examples.
• It finds patterns and rules in them on its own.
• Then it can apply these rules to new situations.

Imagine you are teaching a computer to recognise dogs. Instead of telling it the characteristic features (it has four legs, a tail and barks), you show it thousands of photographs of dogs of various breeds. The system works out for itself what all these pictures have in common and creates its own rules for recognising dogs. This is exactly the principle on which AI works.

We distinguish 3 main types of machine learning.

1. Supervised Learning

Here we teach AI similarly to school:

• We give it examples for which we know the correct answer. For example thousands of pictures clearly labelled: "this is a cat" and "this is a dog".
• AI gradually understands what properties cats have and what properties dogs have. Then it can also recognise new, previously unseen pictures.

This method is used for face recognition, spam filtering or predicting property prices.

2. Unsupervised Learning

With this approach AI works like a detective:

• It receives unlabelled data without correct answers.
• It looks for hidden patterns and groups on its own. For example, it watches people's buying behaviour and finds out which groups of customers have similar preferences.

This way of learning is used for dividing customers into groups, detecting suspicious transactions or reducing the size of data.

3. Reinforcement Learning

This type of learning resembles training a dog with rewards:

• AI performs actions in a certain environment. It receives rewards for good actions and punishments for bad ones.
• Gradually it learns the strategy that brings the most rewards.

This method is used to train self-driving cars, robotic systems or AI that beats professionals in games such as chess or Go.

Neural Networks: The Brain of Artificial Intelligence

At the core of many modern AI systems are neural networks, which are loosely inspired by how the human brain works:

• They consist of interconnected artificial neurons arranged in layers.
• The input layer receives data (e.g. the pixels of an image).
• The hidden layers process the information.
• The output layer provides the result (e.g. this is 95% a cat).

When a neural network contains many layers (often dozens or hundreds), we talk about deep learning. It is precisely deep learning that stands behind the biggest advances in AI over the last decade – from image recognition through language translation to text generation.

How Do Gemini, ChatGPT and Similar AI Systems Work?

One of the best-known examples of artificial intelligence today is large language models such as ChatGPT. These systems use a special type of neural network that lets them understand context in text. Their training takes place in several steps:

1. They learn from a huge amount of text from the internet, books and other sources.
2. They improve based on our ratings and feedback.
3. They learn to create answers that are useful, safe and ethical.

The result is a system that can create texts and answer questions in a way that resembles human communication. It is important to know that ChatGPT and similar systems have no real understanding or consciousness – they just predict very well what text should follow, based on the patterns they have learned.

What Are the Disadvantages of AI?

Despite all the progress, today's artificial intelligence has several important limitations:

• It has no real understanding – It only recognises patterns in data.
• It depends on the quality of the data – If it learns from poor-quality or biased information, its results will be poor too.
• With complex models we often don't know exactly how they arrived at a given result.
• It lacks common sense – Even advanced AI can fail at simple tasks that a small child manages without a problem.
• It brings ethical and societal challenges – From bias in data to questions of privacy and security.

The Difference Between Ready-Made AI and a Custom Solution

When introducing artificial intelligence in companies there are two main approaches:

Ready-Made AI

Tools such as Google Analytics 4 (and its machine learning) or ChatGPT are ready for immediate use. Their advantage is versatility and simplicity. For many companies, however, they may not bring real value, because they are too general.

Advantages:

• Quick deployment,
• lower initial costs,
• minimal technical knowledge for basic use.

Disadvantages:

• Limited customisation,
• inability to solve specific problems,
• may not use the full potential of your data.

Custom Solution (Tailor-Made AI)

These are systems built exactly to the needs of a specific company. For example, in web analytics we can create our own models that better take into account business goals, seasonal trends or specific customer behaviour.

Advantages:

• Customisation exactly to your needs,
• better use of your company's specific data,
• the chance to gain a competitive advantage.

Disadvantages:

• Higher initial costs,
• longer development time,
• need for expert knowledge.

An Example of Using Artificial Intelligence in Practice

Imagine you have an online shop where you sell garden supplies. A ready-made AI solution can tell you the general conversion rate on the market. But a custom-built system can analyse much more specific data:

• The seasonality of garden goods sales in different regions,
• how the weather affects demand for different products,
• forecasts of when a specific customer will need to restock based on their previous purchases.

The result is much more precise targeting of advertising campaigns, better inventory planning and ultimately higher profits.

How to Successfully Introduce Artificial Intelligence in a Company?

If you want to implement artificial intelligence in your company, remember that technology is only one piece of the puzzle. What matters is strategy, quality data and the ability to use the results for actions that move your business forward:

1. Set clear goals – What exactly do you want to solve with AI? What are the main success indicators? Without a clear brief AI cannot deliver useful results.
2. Start with quality data – Make sure you have relevant and ethically obtained data available. Bad data = bad results. Data quality plays a key role in the success of AI projects.
3. Choose the right path – Decide whether a ready-made solution is enough for your specific case or whether you need a custom system. In the long run it is often custom-built systems that distinguish successful companies from average ones.
4. Secure expert knowledge – Either in your company or through external partners
5. Test and evaluate – Continuously measure the results and compare them with your goals.
6. Keep improving the system – AI is not a one-off project, but an ongoing process of learning and refinement.

How Can We Help You with Artificial Intelligence?

We specialise in the practical introduction of AI directly into your business:

• Google Vertex AI Search for Retail – We will set up a system for you that offers customers the products they are really looking for. Your e-shop will learn to understand customers' wishes just like an experienced salesperson. On top of that, all the data stays only yours, nobody else will use it.
• Connection to Gemini AI models – We connect your company data with advanced AI tools for working with text, numbers and images. Imagine every customer receiving a message tailored just for them, automatically. For example: "Hello Mr Novak, we noticed that the lawn mower you bought a year ago will need new oil." A personal approach builds trust and increases sales.
• AI agents for your company – We will train AI assistants for you who will know all your company processes, documentation and data. Your employees will have instant access to information without having to go through hundreds of pages of documents.
• Predictive analytics – Using machine learning we can forecast the future development of your business based on historical data, which lets you make better decisions.

Want to know more about how artificial intelligence can move your business forward? Contact us for a non-binding consultation.

Artificial Intelligence Is a Tool, Not a Miracle

One of the main conclusions is that AI is not an all-powerful solution. For it to really bring value, it is necessary to realise that:

• Artificial intelligence is just a tool – Interpreting results, setting strategy and making decisions is still up to people.
• Humans remain irreplaceable – AI is a tool in human hands, not a replacement for them.
• Data quality is the foundation of success – Data quality directly affects the quality of AI results

The Future of Artificial Intelligence

Artificial intelligence keeps evolving and several trends indicate where it will head in the future:

• AI working with text, image and sound at the same time – Systems that can process different types of information at once.
• AI with smaller data requirements – Methods that can learn even from a smaller number of examples.
• More understandable AI – Models where it will be clearer how they reached their conclusions.
• More ethical AI – Greater emphasis on fair, transparent and ethical use of artificial intelligence.

Decentralised AI – In the future more and more AI functions will move directly onto your devices. This means AI will be able to work even without an internet connection, will be faster and will also respect your privacy much more.

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